AUTOMATED PRESENCE DETECTION AND PRESENCE-RELATED CONTROL WITHIN AN INTELLIGENT CONTROLLER

• US 20140101082A1
• Filed: 12/06/2013
• Published: 04/10/2014
• Est. Priority Date: 09/30/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Nest Labs Incorporated (Alphabet Inc.)
• Inventors: Matsuoka, Yoky, Fisher, Evan J., Malhotra, Mark, Stefanski, Mark D., Sharan, Rangoli, Astier, Frank E.
• Granted Patent: US 9,189,751 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/58 using Internet communication
• CPC Class Codes: F24F 11/61 using timers
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 11/66 Sleep mode
• CPC Class Codes: F24F 11/88 Electrical aspects, e.g. circuits
• CPC Class Codes: F24F 2120/00 Control inputs relating to users or occupants
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05D 23/1902 characterised by the use of a variable reference value
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 12/2829 involving user profiles according to which the execution of a home appliance functionality is automatically triggered
• CPC Class Codes: H04Q 2209/10 using a centralized architecture
• CPC Class Codes: H04Q 9/00 Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom

Abstract

The current application is directed to intelligent controllers that use sensor output and electronically stored information, including one or more of electronically stored rules, parameters, and instructions, to determine whether or not one or more types of entities are present within an area, volume, or environment monitored by the intelligent controllers. The intelligent controllers select operational modes and modify control schedules with respect to the presence and absence of the one or more entities. The intelligent controllers employ feedback information to continuously adjust the electronically stored parameters and rules in order to minimize the number of incorrect inferences with respect to the presence or absence of the one or more entities and in order to maximize the efficiency by which various types of systems controlled by the intelligent controllers carry out selected operational modes.

Claims

1. An intelligent controller comprising:

a processor;

a current control schedule stored in the memory;

one or more sensors;

a memory that stores a control schedule and an indication of a presence state; and

instructions stored within the memory that, when executed by the processor, controls the intelligent controller to

record output from the one or more sensors in memory; and

at intervals,

determine a probability of presence of one or more types of entities within a region of a controlled environment using the recorded sensor output in memory,

update the indication of the presence state, and

upon a change of the indication of the presence state, accordingly adjust the control schedule.

2. The intelligent controller of claim 1

wherein the processor includes one or more of

one or more local processors, and

one or more remote processors,

wherein the memory includes one or more of

one or more local electronic memories,

one or more local mass-storage devices,

one or more remote electronic memories, and

one or more remote mass-storage devices.

3. The intelligent controller of claim 1 wherein the current control schedule stored in the memory includes one or more setpoints, each setpoint associated with a time and one or more parameter values.

4. The intelligent controller of claim 1 wherein the one or more sensors produce output from which the probability of presence of one or more types of entities within a region is estimated by the intelligent controller, the one or more sensors selected from among various types of sensors that respond to entity presence, including:

proximity detectors, including capacitive, capacitive-displacement, conductive, magnetic, optical, thermal, sonar, and other types of proximity detectors;

motion detectors, including passive infrared, ultrasonic, microwave, and tomographic motion detectors;

microphones and other types of acoustic detectors;

charge-coupled detectors;

low-resolution digital cameras;

thermopiles and thermocouples;

carbon dioxide sensors;

water-vapor detectors;

pressure sensors;

flow meters;

security/entry detectors; and

field-strength sensors that detect temporal changes in field strength correlated with presence of entities.

5. The intelligent controller of claim 1 wherein the intelligent controller determines a probability of presence of one or more types of entities within a region using the recorded sensor output as well as using additional electronically accessible information, including one or more of:

direct sensor output;

recorded output from sensors over one or more preceding time intervals;

tables, expressions, and other data that relate sensor output values to presence/no-presence probabilities associated with the sensor outputs;

confidence or reliability information for the one or more sensors;

the current control schedule;

historical control-schedule and setpoint information;

historical presence/no-presence information, including presence/no-presence determinations previously made by the intelligent controller or other intelligent controllers and remote computer systems with which the intelligent controller communicates;

results of various presence-pattern-determining analyses; and

information obtained from remote entities, including data from remote sensors within remote intelligent controllers, data from a remote computing facility, data from various user devices, including cell phones and mobile phones, and data from various smart appliances within the controlled environment.

6. The intelligent controller of claim 1 wherein determining a probability of presence of one or more types of entities within a region further includes determining, for one or more regions and volumes within the controlled environment, one or more of:

one or more presence-probability maps; and

one or more presence-probability scalar values.

7. The intelligent controller of claim 6 wherein the intelligent controller determines the probability of presence of one or more types of entities within one or more regions and volumes within the controlled environment by:

for each of the one or more regions and volumes,

determining a cumulative presence-probability scalar or cumulative presence-probability-map values from a presence-probability scalar or from presence-probability-map values determined from output of each of the one or more sensors; and

applying rules to the cumulative presence-probability scalar or cumulative presence-probability-map values in order to adjust the cumulative presence-probability scalar or cumulative presence-probability-map values for one or more additional considerations based on the additional electronically accessible information.

8. The intelligent controller of claim 1 wherein the intelligent controller updates the indication of the presence state by:

when a probability of presence for a region rises above a first threshold, setting the indication of the presence state for the region to indicate the presence of an entity in the region; and

when a probability of presence for a region falls below a second threshold, setting the indication of the presence state for the region to indicate the absence of an entity in the region.

9. The intelligent controller of claim 1 wherein, upon a change of the indication of the presence state, the intelligent controller accordingly adjusts the control schedule by one or more of:

changing setpoint parameter values;

moving setpoints in time;

deleting setpoints; and

adding setpoints.

10. The intelligent controller of claim 1 wherein the intelligent controller controls one or more of:

an HVAC unit;

a furnace;

an air conditioner;

a heat pump;

an irrigation system;

a pump;

a fan;

one or more light sources;

a machine;

a device;

an organization; and

a system.

11. The intelligent controller of claim 1 wherein the setpoints of the control schedule specify one or more of:

temperature;

flow rate of a liquid or gas;

rate of energy dissipation;

pressure;

current density;

voltage;

a machine setting;

a position of a machine component;

a computational state;

a mechanical state; and

process throughput.

12. An intelligent thermostat comprising:

a processor;

a current control schedule stored in the memory;

one or more sensors;

a schedule interface;

an immediate-control interface;

a memory that stores a control schedule and an indication of a presence state; and

instructions stored within the memory that, when executed by the processor, controls the intelligent thermostat to

record output from the one or more sensors in memory; and

at intervals,

determine a probability of presence of one or more people within a region of a controlled environment using the recorded sensor output in memory,

update the indication of the presence state, and

upon a change of the indication of the presence state, accordingly adjust the control schedule.

13. The intelligent thermostat of claim 12 wherein the indication of a presence state indicates one of:

a home state;

an away-normal state; and

an away-vacation state.

14. The intelligent thermostat of claim 12 wherein the indication of a presence state indicates one of:

a home state;

an away-normal state;

an away-vacation state; and

a sleep state.

15. The intelligent thermostat of claim 14 wherein the intelligent thermostat determines a probability of presence of one or more people within a region using the recorded sensor output as well as using additional electronically accessible information, including one or more of:

direct sensor output;

recorded output from sensors over one or more preceding time intervals;

tables, expressions, and other data that relate sensor output values to presence/no-presence probabilities associated with the sensor outputs;

confidence or reliability information for the one or more sensors;

the current control schedule;

historical control-schedule and setpoint information;

historical presence/no-presence information, including presence/no-presence determinations previously made by the intelligent thermostat or other intelligent thermostats and remote computer systems with which the intelligent thermostat communicates;

results of various presence-pattern-determining analyses; and

information obtained from remote entities, including data from remote sensors within remote intelligent thermostats, data from a remote computing facility, data from various user devices, including cell phones and mobile phones, and data from various smart appliances within the controlled environment.

16. The intelligent thermostat of claim 14 wherein the intelligent thermostat determines the probability of presence of one or more people within one or more regions and volumes within the controlled environment by:

for each of the one or more regions and volumes,

determining a cumulative presence-probability value; and

applying rules to the cumulative presence-probability value in order to adjust the cumulative presence-probability value for one or more additional considerations based on the additional electronically accessible information.

17. The intelligent thermostat of claim 16 wherein the intelligent thermostat updates the indication of the presence state by:

when a probability of presence for a region rises above a first threshold, setting the indication of the presence state for the region to indicate the presence of an entity in the region; and

when a probability of presence for a region falls below a second threshold, setting the indication of the presence state for the region to indicate the absence of an entity in the region.

18. The intelligent thermostat of claim 12 wherein, upon a change of the indication of the presence state, the intelligent thermostat accordingly adjusts the control schedule by one or more of:

changing setpoint parameter values;

moving setpoints in time;

deleting setpoints; and

adding setpoints.

19. A method, incorporated within an intelligent thermostat comprising a processor, a current control schedule stored in the memory, one or more sensors, a schedule interface, an immediate-control interface, and a memory that stores a control schedule and an indication of a presence state, the method comprising:

recording output from the one or more sensors in memory; and

at intervals,

determining a probability of presence of one or more people within a region of a controlled environment using the recorded sensor output stored in memory,

updating the indication of the presence state, and

upon a change of the indication of the presence state, accordingly adjusting the control schedule.

20. The method of claim 19 wherein determining a probability of presence of one or more people within a region of a controlled environment using the recorded sensor output in memory further uses additional electronically accessible information, including one or more of:

direct sensor output;

recorded output from sensors over one or more preceding time intervals;

tables, expressions, and other data that relate sensor output values to presence/no-presence probabilities associated with the sensor outputs;

confidence or reliability information for the one or more sensors;

the current control schedule;

historical control-schedule and setpoint information;

historical presence/no-presence information, including presence/no-presence determinations previously made by the intelligent thermostat or other intelligent thermostats and remote computer systems with which the intelligent thermostat communicates;

results of various presence-pattern-determining analyses; and

information obtained from remote entities, including data from remote sensors within remote intelligent thermostats, data from a remote computing facility, data from various user devices, including cell phones and mobile phones, and data from various smart appliances within the controlled environment.